Aimed to describe trends in length-for-age, weight-for-age, weight-for-length, and early childhood weight gain among US children aged 6 to 23 months from NHANES data ranging from 1976 to 2014. Between 1976–1980 and 2011–2014, there were no significant trends in low or high weight-for-age and weight-for-length, whereas the percent with high length-for-age decreased (5.5% to 3.7%). Non-Hispanic black children gained more weight between birth and survey participation in 2011–2014 versus 1988–1994.Akinbami 2017 (Pediatrics) | PubMed 28213608 | Author Search
Downloadable PDFs for plotting growth for children with Trisomy 21, based on data collected in the Down Syndrome Growing Up Study (DSGS) published in 2015. This study collected data from a convenience sample of patients with Down syndrome.CDC 2017 (Link)
Report on experience using a body-mass index chart that may be better suited for plotting children at the extremes of the growth spectrum. This growth chart uses a “modified z score,” proposed by the CDC, which expresses variations from the median in terms of a unit equal to one half the difference between 0 and +2 z scores (for measures above the mean) and one half the difference between 0 and -2 z scores (for measures below the mean) for any given age and gender.
If one uses modified z score as the y-axis against age, ordinary BMI changes fall along a curve that is much close to a straight line, so outliers should be easier to spot in this circumstance.
Authors illustrate the advantages of using an age-vs-BMI chart with modified z-score isobars over the standard CDC 2000 charts and over the modified charts showing the percentage of the 95th percentile of BMI.Chambers 2017 (Pediatrics) | PubMed 29114063 | Author Search
This analysis of data on 26,480 children and adolescents taken as part of the NHANES study from 1999–2000 through 2011–2012 showed that the overall prevalence of children with biologically implausible body measurements (determined commonly accepted rules involving modified z scores) was 0.9%. Most of these were high values rather than low. Further analysis that correlated these BIVs with other body measurements suggested that the majority of these seemingly anomalous values were accurate. Using these methods to exclude BIVs tends to underestimate the prevalence of obesity in these data.
Computational methods for detecting biologically implausible values in growth data from the Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Describes the calculation of z-scores and ‘modified z-scores’ in the CDC growth chart data published in 2000. A modified z value is defined, for values above the mean, as half of the difference between the value corresponding to a z-score of 2 and the mean. For values below the mean, the modified z-score is half of the difference between the value corresponding to a z-score of 2 and the mean. One expresses the modifies z-score in terms of the modified z-value. For example, for a 4-year old (48.5 months old) boy, the mean BMI is 15.62. The BMI value corresponding to a z-score of -2 is 13.74. So the modified z-value is (15.62 – 13.74)/2 = 0.94. A boy that age with a BMI of 12 would have a modified BMI z-score of (12 – 15.62)/0.94 = -3.85.CDC 2000 (Link)
Reports the development of an automated method for identifying implausible values in pediatric EHR growth (weight and height) data, tested via data points collected in the primary care environment on over 280,000 patiets. The method compares each measurement’s z-score to a weighted moving average of prior measurements. The method had a sensitivity of 97% and a specificity of 90% for identifying implausible values compared to physician judgment, and identified almost all simulated errors.Daymont 2017 (JAMIA) | PubMed 28453637 | Author Search
Analysis of the use of new (2015) Down-syndrome BMI norms compared to standard CDC norms. Concludes that the general CDC norms are a better indicator of excess adiposity than the Down-syndrome-secific ones for DS children 10 years old and up.Hatch-Stein 2016 (Pediatrics) | PubMed 27630073 | Author Search
Based on data from the Pediatrix Medical Group from close to 400,000 infants born in 248 US hospitals from 1998-2006. Comparison to curves used since the 1970s suggests that more modern curves are much more valid to judge growth of premature infants today.Olsen 2010 (Pediatrics) | PubMed 20100760 | Author Search
Based on data from the Vermont Oxford Network from 183,243 racially diverse, singleton infants born in the US without congenital malformations. Tends to represent smaller infants compared to older charts; this is likely due to the increased survival of small infants over time. Provides norms for Asian, Black, and White infants.Boghossian 2016 (Pediatrics) | PubMed 27940694 | Author Search